Arbitrary resizing of images in DCT domain using lapped transforms

An arbitrary L/M-fold image resizing method using lapped transforms is presented. The resizing operation is carried out in the lapped transform domain, by converting the images in the discrete cosine transform (DCT) domain into those in the lapped transform domain and vice versa. The proposed method provides visually fine images, while reducing the blocking effect to a very low level for images compressed at low bit rates.     

Integer lapped transforms and their applications to image coding

This paper proposes new integer approximations of the lapped transforms, called the integer lapped transforms (ILT), and studies their applications to image coding. The ILT are derived from a set of orthogonal sinusoidal transforms having short integer coefficients, which can be implemented with simple integer arithmetic. By employing the same scaling constants in these integer sinusoidal transforms, integer versions of the lapped orthogonal transform (LOT), the lapped biorthogonal transform (LBT), and the hierarchical lapped biorthogonal transform (HLBT) are developed. The ILTs with 5-b integer coefficients are found to have similar coding gain (within 0.06 dB) and image coding performances as their real-valued counterparts. Furthermore, by representing these integer coefficients as sum of powers-of-two coefficients (SOPOT), multiplier-less lapped transforms with very low implementation complexity are obtained. In particular, the implementation of the eight-channel multiplier-less integer LOT (ILOT), LBT (ILBT), and HLBT (IHLBT) require 90 additions and 44 shifts, 98 additions and 59 shifts, and 70 additions and 38 shifts, respectively.     

On lapped orthogonal transforms [signal coding applications]

The energy compaction performance of several lapped orthogonal transforms (LOTs) are presented. It is shown that the LOT outperforms the conventional block transforms for all the cases considered. The performance of the poorly performing block transforms for high correlation sources increased dramatically in their LOT versions. It is found that the energy compaction performance of the LOT versions of the different block transforms considered is about the same. Therefore, the choice of LOT is based on the efficiency of the transform algorithm. The LOT is an alternative to the block transforms for signal coding applications. Also, the blocking effect is reduced with the increase in the computational complexity of the transform algorithm     

Image coding using lapped biorthogonal transform

The wireless sensor network utilizes image compression algorithms like JPEG, JPEG2000, and SPIHT for image transmission with high coding efficiency. During compression, discrete cosine transform (DCT)–based JPEG has blocking artifacts at low bit-rates. But this effect is reduced by discrete wavelet transform (DWT)–based JPEG2000 and SPIHT algorithm but it possess high computational complexity. This paper proposes an efficient lapped biorthogonal transform (LBT)–based low-complexity zerotree codec (LZC), an entropy coder for image coding algorithm to achieve high compression. The LBT-LZC algorithm yields high compression, better visual quality with low computational complexity. The performance of the proposed method is compared with other popular coding schemes based on LBT, DCT and wavelet transforms. The simulation results reveal that the proposed algorithm reduces the blocking artifacts and achieves high compression. Besides, it is analyzed for noise resilience.     

Design of lapped orthogonal transforms

We investigate the design of lapped orthogonal transforms for data compression of images. We present some properties and new results of paraunitary filter banks. We concentrate on the case where the filter length L=2K, where K is the number of channels. The aim is to design perceptually relevant filters, i.e., linear-phase filters that smoothly decay to zero at the boundaries.     

Joint source/channel coding for multiple images

A joint source/channel coding algorithm is proposed for the transmission of multiple image sources over memoryless channels. The proposed algorithm uses a quality scalable image coder to optimally allocate a limited bit budget among all the sources to achieve the optimal overall distortion reduction for the multiple reconstructed images. In addition to the conventional un gain, it provides channel multiplexing gain, which can be much more significant. Furthermore, an extended scheme is proposed to provide flexibility between the optimization performance and complexity.     

Variable-length lapped transforms with a combination of multiple synthesis filter banks for image coding.

A class of lapped transforms for image coding, which are characterized by variable-length synthesis filters, is introduced. In this class, the synthesis filter bank (FB) is first defined with an arbitrary combination of finite impulse response synthesis filters of perfect reconstruction FBs. An analysis FB is then obtained using direct matrix inversion or iterative implementation of Neumann series expansion. Moreover, to improve compression, we introduce a unitary transform that follows the analysis FB. This class enables a greater freedom of design than previously presented variable-length lapped transforms. We illustrate several design examples and present experimental results for image coding, which indicate that the proposed transforms are promising and comparable with conventional subband transforms including wavelets.     

静态图像中的人体行为分类研究

静态图像中人体行为分类的一般方法是先手动标定出行为对象,再单独对行为对象进行特征提取和分析,不仅费时费力还丢失了场景信息。针对此问题提出了结合场景特征与行为对象特征的图像表示方法,充分利用图像的所有信息。此外为了减小量化误差,在特征编码阶段,采用局部约束线性编码(Locality-constrained Linear Coding,LLC)算法,并将其应用在空间金字塔模型的向量量化中。该算法与传统的矢量量化算法和稀疏编码算法相比,能够降低量化误差。最后在Stanford 40 Action数据集上对文中方法进行实验和验证,结果表明,结合场景特征与行为对象特征并使用LLC编码算法能够获得更好的分类效果。     

A family of lapped regular transforms with integer coefficients

Invertible transforms with integer coefficients are highly desirable because of their fast, efficient, VLSI-suitable implementations and their lossless coding capability. In this paper, a large class of lapped regular transforms with integer coefficients (ILT) is presented. Regularity constraints are also taken into account to provide smoother reconstructed signals. In other words, this ILT family can be considered to be an M-band biorthogonal wavelet with integer coefficients. The ILT also possesses a fast and efficient lattice that structurally enforces both linear-phase and exact reconstruction properties. Preliminary image coding experiments show that the ILT yields comparable objective and subjective performance to those of popular state-of-the-art transforms with floating-point coefficients     

Joint source/channel coding using arithmetic codes

Reserving space fur a symbol that is not in the source alphabet has been shown to provide excellent error detection. In this paper, we show how to exploit this capability using two sequential decoder structures to provide powerful error correction capability. This joint source/channel coder design provides significant packet loss recovery with minimal rate overhead, and compares favorably with conventional schemes     

Design of orthogonal and biorthogonal lapped transforms satisfyingperception related constraints

We propose a new efficient method for the design of orthogonal and biorthogonal lapped transforms for image coding applications. It is shown how perception related constraints such as decay and smoothness of the filters' impulse responses can be incorporated in the optimization procedure. A decomposition of lapped transforms (orthogonal and biorthogonal) with 50% overlap leads to an efficient recursive optimization procedure, which is robust with respect to initial solutions. The importance of this decomposition lies in the fact that it allows to decouple the design of the even-symmetric and the odd-symmetric filters and hence drastically reduces the number of variables to be optimized. It furthermore reveals all the variables predetermined by perception related and coding-efficiency related constraints imposed on the filters. We present design and coding examples demonstrating the perceptual performance and the rate distortion performance of the resulting transforms.     

Image coding using wavelet transforms and entropy-constrainedtrellis-coded quantization

The discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multi-resolution approximations. Multi-resolution decomposition schemes have proven to be very effective for high-quality, low bit-rate image coding. In this work, we investigate the use of entropy-constrained trellis-coded quantization (ECTCQ) for encoding the wavelet coefficients of both monochrome and color images. ECTCQ is known as an effective scheme for quantizing memoryless sources with low to moderate complexity, The ECTCQ approach to data compression has led to some of the most effective source codes found to date for memoryless sources. Performance comparisons are made using the classical quadrature mirror filter bank of Johnston and nine-tap spline filters that were built from biorthogonal wavelet bases. We conclude that the encoded images obtained from the system employing nine-tap spline filters are marginally superior although at the expense of additional computational burden. Excellent peak-signal-to-noise ratios are obtained for encoding monochrome and color versions of the 512x512 "Lenna" image. Comparisons with other results from the literature reveal that the proposed wavelet coder is quite competitive.     

Progressive medical image coding using binary wavelet transforms

In this paper, a new algorithm for progressive medical image coding is presented. An 8-bit gray scale image is divided into eight binary bit-planes, and then, binary wavelet transform is performed on each bit-plane to extract the three-level multi-resolution binary wavelet transformed images. Starting from the most significant bit-plane, each bit-plane is encoded using quadtree-based partitioning scheme to exploit the energy concentration in the high-frequency subbands. Experiments are conducted on ultrasound, MRI and CT images to prove the effectiveness of the proposed algorithm. The results show a significant improvement in terms of bit-rate for the required peak signal-to-noise ratio and correlation coefficient as compared to the existing state-of-art progressive image coding methods.     

Scalable image coding using reversible integer wavelet transforms

Reversible integer wavelet transforms allow both lossless and lossy decoding using a single bitstream. We present a new fully scalable image coder and investigate the lossless and lossy performance of these transforms in the proposed coder. The lossless compression performance of the presented method is comparable to JPEG-LS. The lossy performance is quite competitive with other efficient lossy compression methods.     

Compression of multispectral images by spectral classification andtransform coding

This paper presents a new technique for the compression of multispectral images, which relies on the segmentation of the image into regions of approximately homogeneous land cover. The rationale behind this approach is that, within regions of the same land cover, the pixels have stationary statistics and are characterized by mostly linear dependency, contrary to what usually happens for unsegmented images. Therefore, by applying conventional transform coding techniques to homogeneous groups of pixels, the proposed algorithm is able to effectively exploit the statistical redundancy of the image, thereby improving the rate distortion performance. The proposed coding strategy consists of three main steps. First, each pixel is classified by vector quantizing its spectral response vector, so that both a reliable classification and a minimum distortion encoding of each vector are obtained. Then, the classification map is entropy encoded and sent as side information, Finally, the residual vectors are grouped according to their classes and undergo Karhunen-Loeve transforming in the spectral domain and discrete cosine transforming in the spatial domain. Numerical experiments on a six-band thematic mapper image show that the proposed technique outperforms the conventional transform coding technique by 1 to 2 dB at all rates of interest.     

Image coding using translation invariant wavelet transforms withsymmetric extensions

In this correspondence, we address the problem of translation sensitivity of conventional wavelet transforms for two-dimensional (2-D) signals. We propose wavelet transform algorithms that achieve the following desirable properties simultaneously: (i) translation invariance, (ii) reduced edge effects, and (iii) size-limitedness. We apply this translation invariant biorthogonal wavelet transform with symmetric extensions to image coding applications with good results.     

Source coding, channel coding and modulation techniques used in theATVA-progressive system

The ATVA-progressive system, an all-digital high-definition television system that delivers very high quality pictures to the home within a 6-MHz channel in the presence of noise, ghosts, and frequency distortion is described. The system achieves a high degree of data compression by means of motion compensation and transform/subband coding, and only transform/subband coefficients with significant energy are transmitted. For terrestrial transmission, an all-digital format that uses a single carrier with double sideband suppressed carrier quadrature modulation is used. The source signal, source coding, channel coding, modulation, and performance of the system are discussed     

Adaptive transforms for image coding using spatially varyingwavelet packets

We introduce a novel, adaptive image representation using spatially varying wavelet packets (WPs), Our adaptive representation uses the fast double-tree algorithm introduced previously (Herley et al., 1993) to optimize an operational rate-distortion (R-D) cost function, as is appropriate for the lossy image compression framework. This involves jointly determining which filter bank tree (WP frequency decomposition) to use, and when to change the filter bank tree (spatial segmentation). For optimality, the spatial and frequency segmentations must be done jointly, not sequentially. Due to computational complexity constraints, we consider quadtree spatial segmentations and binary WP frequency decompositions (corresponding to two-channel filter banks) for application to image coding. We present results verifying the usefulness and versatility of this adaptive representation for image coding using both a first-order entropy rate-measure-based coder as well as a powerful space-frequency quantization-based (SPQ-based) wavelet coder introduced by Xiong et al. (1993).